2  Vectors

Author

Vladimir Buskin

2.1 Suggested reading

  • Gries (2013: Chapter 2)

  • James et al. (2021: Chapter 2.3)

  • Winter (2019: Chapter 1)

2.2 Word frequencies I

You are given the following token counts of English verb lemmas in the International Corpus of English:

Lemma Frequency
start 418
enjoy 139
begin 337
help 281

It is always a good idea to visualise frequency data in some way. Quite conveniently, R happens to provide us with an abundance of plotting functions. To create a two-dimensional plot, we need to generate two objects in R: one for the individual lemmas and one for the frequency counts.

Let’s combine the lemmas start, enjoy, begin and help using R’s c() function and store them in an object lemma. Enter the following line into a new R script and click on Run (or simply press Ctrl+Enter/Cmd+Enter).

lemma <- c("start", "enjoy", "begin", "help")

We can now do the same for the frequency information:

frequency <- c(418, 139, 337, 281)

Letters and numbers represent two distinct data types in R. Anything that should be understood as a simple sequence of letters or words must be enclosed by quotation marks "...". An expression such as start will then be evaluated as a string.

Numbers (or integers), by contrast, appear without quotation marks.

Our linguistic data is now stored in two variables lemma and frequency, which you can conceptualise as virtual container-like objects. You will also notice that they are now showing in the Environment tab in the top right corner of RStudio.

The combination of categorical labels and numeric information renders our data ideally suited for a barplot. R’s most basic barplot function (which is, unsurprisingly, called barplot()) needs at the very least …

  • a height argument, i.e., our y-axis values and

  • a names.arg argument, i.e., our x-axis labels.

barplot(frequency, names.arg = lemma, col = "skyblue")

After some tinkering, our plot looks more presentable:

barplot(frequency, names.arg = lemma, 
        main = "Frequency of Lemmas", # title
        xlab = "Lemmas",  # label for x-axis
        ylab = "Frequency", # label for y-axis
        col = "steelblue") # color

In R, everything followed by the hashtag # will be interpreted as a comment and won’t be evaluated by the R compiler. While comments don’t affect the output of our code in the slightest, they are crucial to any kind of programming project.

Adding prose annotations to your code will make not only it easier to understand for others but also for your future self. Poor documentation is a common, yet unnecessary source of frustration for all parties involved …

In RStudio, you now have the option to save the plot to your computer. Once the figure has appeared in your “Plots” panel, you can click on “Export” in the menu bar below and proceed to choose the desired output format and file directory.

2.3 Some technical details

The example above demonstrates one of the most important data structures in R: Vectors. They form the cornerstone of various more complex objects such as data frames, and are essential to handling large data sets (e.g., corpora). And yet, vectors are very simple in that they merely constitute one-dimensional sequences of characters or numbers – no more, no less.

print(lemma)
[1] "start" "enjoy" "begin" "help" 
print(frequency)
[1] 418 139 337 281

The individual elements in these two vectors are not randomly jumbling around in virtual space, but are in fact following a clear order. Each element has an “ID” (or index), by which we can access it. For example, if we want to print the first lemma in our lemma variable, we can use this notation:

lemma[1]
[1] "start"

Similarly, we can subset frequency according to, for example, its third element:

frequency[3]
[1] 337

We can also obtain entire ranges of elements, such as everything from the second to the fourth one:

frequency[2:4]
[1] 139 337 281

2.4 Practical

  1. Create a vector that lists the third person personal pronouns of English (subject and object forms). Store them in a variable pp3.
Solution: 
pp3 <- c("he", "she", "it", "him", "her", "they", "them")

  1. Now print …

    • … the fourth element in pp3.
    Solution: 
    print(pp3[4]) # or simply

pp3[4]
    • … elements 3 through 5.
    Solution: 
    pp3[3:5]
    • … all elements.
    Solution: 
    pp3
    • … elements 1, 3 and 5.
    Solution: 
    pp3[c(1, 3, 5)]

  2. When working with large datasets, we often don’t know whether an element is in the vector to begin with, let alone its position. For instance, if we wanted to check whether they is in pp3 or not, we could use the handy notation below, returning a TRUE or FALSE value:

"they" %in% pp3
[1] TRUE

Ascertain whether the following items are in pp3:

  • him

    Solution: 
    "him" %in% pp3 # TRUE

  • you

    Solution: 
    "you" %in% pp3 # FALSE

  • it and them

    Solution: 
    c("it", "them") %in% pp3 # TRUE TRUE

  • we, us and me

    Solution: 
    c("we", "us", "them") %in% pp3 # FALSE FALSE TRUE
  1. Once we are sure that an element is in the vector of interest, another common problem that arises is finding its location. Luckily, R has got us covered! The which() function returns the index of an element. You can read this notation as “Which element in pp3 is they?”. The output suggests that is in position 6. Note that the number obtained depends on the order of elements you’ve chosen when creating pp3.
which(pp3 == "they") # Note the two equal signs == !
[1] 6

Find the locations of it and them in pp3.

Solution: 
# "him"
which(pp3 == "it")

# "you"
which(pp3 == "them")